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Scientists have controlled a mouse’s neurons with a wireless device (and unleashed some paranoid fantasies? well, mine if no one else’s) according to a July 16, 2015 news item on Nanowerk (Note: A link has been removed),

A study showed that scientists can wirelessly determine the path a mouse walks with a press of a button. Researchers at the Washington University School of Medicine, St. Louis, and University of Illinois, Urbana-Champaign, created a remote controlled, next-generation tissue implant that allows neuroscientists to inject drugs and shine lights on neurons deep inside the brains of mice. The revolutionary device is described online in the journal Cell (“Wireless Optofluidic Systems for Programmable In Vivo Pharmacology and Optogenetics”). Its development was partially funded by the [US] National Institutes of Health [NIH].

The researchers have made an image/illustration of the probe available,

Mind Bending Probe Scientists used soft materials to create a brain implant a tenth the width of a human hair that can wirelessly control neurons with lights and drugs. Courtesy of Jeong lab, University of Colorado Boulder.

“It unplugs a world of possibilities for scientists to learn how brain circuits work in a more natural setting.” said Michael R. Bruchas, Ph.D., associate professor of anesthesiology and neurobiology at Washington University School of Medicine and a senior author of the study.

The Bruchas lab studies circuits that control a variety of disorders including stress, depression, addiction, and pain. Typically, scientists who study these circuits have to choose between injecting drugs through bulky metal tubes and delivering lights through fiber optic cables. Both options require surgery that can damage parts of the brain and introduce experimental conditions that hinder animals’ natural movements.

To address these issues, Jae-Woong Jeong, Ph.D., a bioengineer formerly at the University of Illinois at Urbana-Champaign, worked with Jordan G. McCall, Ph.D., a graduate student in the Bruchas lab, to construct a remote controlled, optofluidic implant. The device is made out of soft materials that are a tenth the diameter of a human hair and can simultaneously deliver drugs and lights.

“We used powerful nano-manufacturing strategies to fabricate an implant that lets us penetrate deep inside the brain with minimal damage,” said John A. Rogers, Ph.D., professor of materials science and engineering, University of Illinois at Urbana-Champaign and a senior author. “Ultra-miniaturized devices like this have tremendous potential for science and medicine.”

With a thickness of 80 micrometers and a width of 500 micrometers, the optofluidic implant is thinner than the metal tubes, or cannulas, scientists typically use to inject drugs. When the scientists compared the implant with a typical cannula they found that the implant damaged and displaced much less brain tissue.

The scientists tested the device’s drug delivery potential by surgically placing it into the brains of mice. In some experiments, they showed that they could precisely map circuits by using the implant to inject viruses that label cells with genetic dyes. In other experiments, they made mice walk in circles by injecting a drug that mimics morphine into the ventral tegmental area (VTA), a region that controls motivation and addiction.

The researchers also tested the device’s combined light and drug delivery potential when they made mice that have light-sensitive VTA neurons stay on one side of a cage by commanding the implant to shine laser pulses on the cells. The mice lost the preference when the scientists directed the device to simultaneously inject a drug that blocks neuronal communication. In all of the experiments, the mice were about three feet away from the command antenna.

“This is the kind of revolutionary tool development that neuroscientists need to map out brain circuit activity,” said James Gnadt, Ph.D., program director at the NIH’s National Institute of Neurological Disorders and Stroke (NINDS). “It’s in line with the goals of the NIH’s BRAIN Initiative.”

The researchers fabricated the implant using semi-conductor computer chip manufacturing techniques. It has room for up to four drugs and has four microscale inorganic light-emitting diodes. They installed an expandable material at the bottom of the drug reservoirs to control delivery. When the temperature on an electric heater beneath the reservoir rose then the bottom rapidly expanded and pushed the drug out into the brain.

“We tried at least 30 different prototypes before one finally worked,” said Dr. McCall.

“This was truly an interdisciplinary effort,” said Dr. Jeong, who is now an assistant professor of electrical, computer, and energy engineering at University of Colorado Boulder. “We tried to engineer the implant to meet some of neurosciences greatest unmet needs.”

In the study, the scientists provide detailed instructions for manufacturing the implant.

“A tool is only good if it’s used,” said Dr. Bruchas. “We believe an open, crowdsourcing approach to neuroscience is a great way to understand normal and healthy brain circuitry.”

This story take a few twists and turns. First, ‘brain chips’ as they’re sometimes called would allow, theoretically, computers to learn and function like human brains. (Note: There’s another type of ‘brain chip’ which could be implanted in human brains to help deal with diseases such as Parkinson’s and Alzheimer’s. *Today’s [June 26, 2015] earlier posting about an artificial neuron points at some of the work being done in this areas.*)

Returning to the ‘brain ship’ at hand. Second, there’s a company called BrainChip, which has one patent and another pending for, yes, a ‘brain chip’.

The company, BrainChip, founded in Australia and now headquartered in California’s Silicon Valley, recently sparked some investor interest in Australia. From an April 7, 2015 article by Timna Jacks for the Australian Financial Review,

Former mining stock Aziana Limited has whet Australian investors’ appetite for science fiction, with its share price jumping 125 per cent since it announced it was acquiring a US-based tech company called BrainChip, which promises artificial intelligence through a microchip that replicates the neural system of the human brain.

Shares in the company closed at 9¢ before the Easter long weekend, having been priced at just 4¢ when the backdoor listing of BrainChip was announced to the market on March 18.

Creator of the patented digital chip, Peter Van Der Made told The Australian Financial Review the technology has the capacity to learn autonomously, due to its composition of 10,000 biomimic neurons, which, through a process known as synaptic time-dependent plasticity, can form memories and associations in the same way as a biological brain. He said it works 5000 times faster and uses a thousandth of the power of the fastest computers available today.

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Mr Van Der Made is inviting technology partners to license the technology for their own chips and products, and is donating the technology to university laboratories in the US for research.

The Netherlands-born Australian, now based in southern California, was inspired to create the brain-like chip in 2004, after working at the IBM Internet Security Systems for two years, where he was chief scientist for behaviour analysis security systems. …

A June 23, 2015 article by Tony Malkovic on phys.org provide a few more details about BrainChip and about the deal,

Mr Van der Made and the company, also called BrainChip, are now based in Silicon Valley in California and he returned to Perth last month as part of the company’s recent merger and listing on the Australian Stock Exchange.

He says BrainChip has the ability to learn autonomously, evolve and associate information and respond to stimuli like a brain.

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Mr Van der Made says the company’s chip technology is more than 5,000 faster than other technologies, yet uses only 1/1,000th of the power.

“It’s a hardware only solution, there is no software to slow things down,” he says.

“It doesn’t executes instructions, it learns and supplies what it has learnt to new information.

…

“BrainChip is on the road to position itself at the forefront of artificial intelligence,” he says.

“We have a clear advantage, at least 10 years, over anybody else in the market, that includes IBM.”

BrainChip is aiming at the global semiconductor market involving almost anything that involves a microprocessor.

BrainChip’s inventor, Peter van der Made, has created an exciting new Spiking Neural Networking technology that has the ability to learn autonomously, evolve and associate information just like the human brain. The technology is developed as a digital design containing a configurable “sea of biomimic neurons’.

The technology is fast, completely digital, and consumes very low power, making it feasible to integrate large networks into portable battery-operated products, something that has never been possible before.

BrainChip neurons autonomously learn through a process known as STDP (Synaptic Time Dependent Plasticity). BrainChip’s fully digital neurons process input spikes directly in hardware. Sensory neurons convert physical stimuli into spikes. Learning occurs when the input is intense, or repeating through feedback and this is directly correlated to the way the brain learns.

Computing Artificial Neural Networks (ANNs)

The brain consists of specialized nerve cells that communicate with one another. Each such nerve cell is called a Neuron,. The inputs are memory nodes called synapses. When the neuron associates information, it produces a ‘spike’ or a ‘spike train’. Each spike is a pulse that triggers a value in the next synapse. Synapses store values, similar to the way a computer stores numbers. In combination, these values determine the function of the neural network. Synapses acquire values through learning.

In Artificial Neural Networks (ANNs) this complex function is generally simplified to a static summation and compare function, which severely limits computational power. BrainChip has redefined how neural networks work, replicating the behaviour of the brain. BrainChip’s artificial neurons are completely digital, biologically realistic resulting in increased computational power, high speed and extremely low power consumption.

The Problem with Artificial Neural Networks

Standard ANNs, running on computer hardware are processed sequentially; the processor runs a program that defines the neural network. This consumes considerable time and because these neurons are processed sequentially, all this delayed time adds up resulting in a significant linear decline in network performance with size.

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BrainChip neurons are all mapped in parallel. Therefore the performance of the network is not dependent on the size of the network providing a clear speed advantage. So because there is no decline in performance with network size, learning also takes place in parallel within each synapse, making STDP learning very fast.

A hardware solution

BrainChip’s digital neural technology is the only custom hardware solution that is capable of STDP learning. The hardware requires no coding and has no software as it evolves learning through experience and user direction.

The BrainChip neuron is unique in that it is completely digital, behaves asynchronously like an analog neuron, and has a higher level of biological realism. It is more sophisticated than software neural models and is many orders of magnitude faster. The BrainChip neuron consists entirely of binary logic gates with no traditional CPU core. Hence, there are no ‘programming’ steps. Learning and training takes the place of programming and coding. Like of a child learning a task for the first time.

Software ‘neurons’, to compromise for limited processing power, are simplified to a point where they do not resemble any of the features of a biological neuron. This is due to the sequential nature of computers, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits. In contrast, the brain’s network is parallel and processes the equivalent of millions of data bits simultaneously.

A significantly faster technology

Performing emulation in digital hardware has distinct advantages over software. As software is processed sequentially, one instruction at a time, Software Neural Networks perform slower with increasing size. Parallel hardware does not have this problem and maintains the same speed no matter how large the network is. Another advantage of hardware is that it is more power efficient by several orders of magnitude.

The speed of the BrainChip device is unparalleled in the industry.

For large neural networks a GPU (Graphics Processing Unit) is ~70 times faster than the Intel i7 executing a similar size neural network. The BrainChip neural network is faster still and takes far fewer CPU (Central Processing Unit) cycles, with just a little communication overhead, which means that the CPU is available for other tasks. The BrainChip network also responds much faster than a software network accelerating the performance of the entire system.

The BrainChip network is completely parallel, with no sequential dependencies. This means that the network does not slow down with increasing size.

Endorsed by the neuroscience community

A number of the world’s pre-eminent neuroscientists have endorsed the technology and are agreeing to joint develop projects.

BrainChip has the potential to become the de facto standard for all autonomous learning technology and computer products.

Patented

BrainChip’s autonomous learning technology patent was granted on the 21st September 2008 (Patent number US 8,250,011 “Autonomous learning dynamic artificial neural computing device and brain inspired system”). BrainChip is the only company in the world to have achieved autonomous learning in a network of Digital Neurons without any software.

A prototype Spiking Neuron Adaptive Processor was designed as a ‘proof of concept’ chip.

The first tests were completed at the end of 2007 and this design was used as the foundation for the US patent application which was filed in 2008. BrainChip has also applied for a continuation-in-part patent filed in 2012, the “Method and System for creating Dynamic Neural Function Libraries”, US Patent Application 13/461,800 which is pending.

Van der Made doesn’t seem to have published any papers on this work and the description of the technology provided on the website is frustratingly vague. There are many acronyms for processes but no mention of what this hardware might be. For example, is it based on a memristor or some kind of atomic ionic switch or something else altogether?

It would be interesting to find out more but, presumably, van der Made, wishes to withhold details. There are many companies following the same strategy while pursuing what they view as a business advantage.

Should you need any electrode implants for your neurons at some point in the future, it’s possible they could be coated with gold. Researchers at the Lawrence Livermore National Laboratory (LLNL) and at the University of California at Davis (UC Davis) have discovered that electrodes covered in nanoporous gold could prevent scarring (from a May 5, 2015 news item on Azonano),

A team of researchers from Lawrence Livermore and UC Davis have found that covering an implantable neural electrode with nanoporous gold could eliminate the risk of scar tissue forming over the electrode’s surface.

The team demonstrated that the nanostructure of nanoporous gold achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Close physical coupling between neurons and the electrode plays a crucial role in recording fidelity of neural electrical activity.

Neural interfaces (e.g., implantable electrodes or multiple-electrode arrays) have emerged as transformative tools to monitor and modify neural electrophysiology, both for fundamental studies of the nervous system, and to diagnose and treat neurological disorders. These interfaces require low electrical impedance to reduce background noise and close electrode-neuron coupling for enhanced recording fidelity.

Designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. An important obstacle in maintaining robust neuron-electrode coupling is the encapsulation of the electrode by scar tissue.

Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface, which is an obstacle to reliable neuron−electrode coupling.

However, the team found that nanoporous gold, produced by an alloy corrosion process, is a promising candidate to reduce scar tissue formation on the electrode surface solely through topography by taking advantage of its tunable length scale.

“Our results show that nanoporous gold topography, not surface chemistry, reduces astrocyte surface coverage,” said Monika Biener, one of the LLNL authors of the paper.

Nanoporous gold has attracted significant interest for its use in electrochemical sensors, catalytic platforms, fundamental structure−property studies at the nanoscale and tunable drug release. It also features high effective surface area, tunable pore size, well-defined conjugate chemistry, high electrical conductivity and compatibility with traditional fabrication techniques.

“We found that nanoporous gold reduces scar coverage but also maintains high neuronal coverage in an in vitro neuron-glia co-culture model,” said Juergen Biener, the other LLNL author of the paper. “More broadly, the study demonstrates a novel surface for supporting neuronal cultures without the use of culture medium supplements to reduce scar overgrowth.”

The image depicts a neuronal network growing on a novel nanotextured gold electrode coating. The topographical cues presented by the coating preferentially favor spreading of neurons as opposed to scar tissue. This feature has the potential to enhance the performance of neural interfaces. Image by Ryan Chen/LLNL.

The decades worth of data that has been collected about the billions of neurons in the brain is astounding. To help scientists make sense of this “brain big data,” researchers at Carnegie Mellon University have used data mining to create http://www.neuroelectro.org, a publicly available website that acts like Wikipedia, indexing physiological information about neurons.

The site will help to accelerate the advance of neuroscience research by providing a centralized resource for collecting and comparing data on neuronal function. A description of the data available and some of the analyses that can be performed using the site are published online by the Journal of Neurophysiology

The neurons in the brain can be divided into approximately 300 different types based on their physical and functional properties. Researchers have been studying the function and properties of many different types of neurons for decades. The resulting data is scattered across tens of thousands of papers in the scientific literature. Researchers at Carnegie Mellon turned to data mining to collect and organize these data in a way that will make possible, for the first time, new methods of analysis.

“If we want to think about building a brain or re-engineering the brain, we need to know what parts we’re working with,” said Nathan Urban, interim provost and director of Carnegie Mellon’s BrainHubSM neuroscience initiative. “We know a lot about neurons in some areas of the brain, but very little about neurons in others. To accelerate our understanding of neurons and their functions, we need to be able to easily determine whether what we already know about some neurons can be applied to others we know less about.”

Shreejoy J. Tripathy, who worked in Urban’s lab when he was a graduate student in the joint Carnegie Mellon/University of Pittsburgh Center for the Neural Basis of Cognition (CNBC) Program in Neural Computation, selected more than 10,000 published papers that contained physiological data describing how neurons responded to various inputs. He used text mining algorithms to “read” each of the papers. The text mining software found the portions of each paper that identified the type of neuron studied and then isolated the electrophysiological data related to the properties of that neuronal type. It also retrieved information about how each of the experiments in the literature was completed, and corrected the data to account for any differences that might be caused by the format of the experiment. Overall, Tripathy, who is now a postdoc at the University of British Columbia, was able to collect and standardize data for approximately 100 different types of neurons, which he published on the website http://www.neuroelectro.org.

Since the data on the website was collected using text mining, the researchers realized that it was likely to contain errors related to extraction and standardization. Urban and his group validated much of the data, but they also created a mechanism that allows site users to flag data for further evaluation. Users also can contribute new data with minimal intervention from site administrators, similar to Wikipedia.

“It’s a dynamic environment in which people can collect, refine and add data,” said Urban, who is the Dr. Frederick A. Schwertz Distinguished Professor of Life Sciences and a member of the CNBC. “It will be a useful resource to people doing neuroscience research all over the world.”

Ultimately, the website will help researchers find groups of neurons that share the same physiological properties, which could provide a better understanding of how a neuron functions. For example, if a researcher finds that a type of neuron in the brain’s neocortex fires spontaneously, they can look up other neurons that fire spontaneously and access research papers that address this type of neuron. Using that information, they can quickly form hypotheses about whether or not the same mechanisms are at play in both the newly discovered and previously studied neurons.

To demonstrate how neuroelectro.org could be used, the researchers compared the electrophysiological data from more than 30 neuron types that had been most heavily studied in the literature. These included pyramidal neurons in the hippocampus, which are responsible for memory, and dopamine neurons in the midbrain, thought to be responsible for reward-seeking behaviors and addiction, among others. The site was able to find many expected similarities between the different types of neurons, and some similarities that were a surprise to researchers. Those surprises represent promising areas for future research.

In ongoing work, the Carnegie Mellon researchers are comparing the data on neuroelectro.org with other kinds of data, including data on neurons’ patterns of gene expression. For example, Urban’s group is using another publicly available resource, the Allen Brain Atlas, to find whether groups of neurons with similar electrical function have similar gene expression.

“It would take a lot of time, effort and money to determine both the physiological properties of a neuron and its gene expression,” Urban said. “Our website will help guide this research, making it much more efficient.”

The researchers have produced a brief video describing neurons and their project,

Michael Berger has written another of his Nanowerk Spotlight articles focussing on neuromorphic engineering and the concept of a brain-on-a-chip bringing it up-to-date April 2014 style.

It’s a topic he and I have been following (separately) for years. Berger’s April 4, 2014 Brain-on-a-chip Spotlight article provides a very welcome overview of the international neuromorphic engineering effort (Note: Links have been removed),

Constructing realistic simulations of the human brain is a key goal of the Human Brain Project, a massive European-led research project that commenced in 2013.

The Human Brain Project is a large-scale, scientific collaborative project, which aims to gather all existing knowledge about the human brain, build multi-scale models of the brain that integrate this knowledge and use these models to simulate the brain on supercomputers. The resulting “virtual brain” offers the prospect of a fundamentally new and improved understanding of the human brain, opening the way for better treatments for brain diseases and for novel, brain-like computing technologies.

Several years ago, another European project named FACETS (Fast Analog Computing with Emergent Transient States) completed an exhaustive study of neurons to find out exactly how they work, how they connect to each other and how the network can ‘learn’ to do new things. One of the outcomes of the project was PyNN, a simulator-independent language for building neuronal network models.

Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain’s abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size – basically a brain-on-a-chip. Already, scientists are working hard on laying the foundations for what is called neuromorphic engineering – a new interdisciplinary discipline that includes nanotechnologies and whose goal is to design artificial neural systems with physical architectures similar to biological nervous systems.

Several research projects funded with millions of dollars are at work with the goal of developing brain-inspired computer architectures or virtual brains: DARPA’s SyNAPSE, the EU’s BrainScaleS (a successor to FACETS), or the Blue Brain project (one of the predecessors of the Human Brain Project) at Switzerland’s EPFL [École Polytechnique Fédérale de Lausanne].

Berger goes on to describe the raison d’être for neuromorphic engineering (attempts to mimic biological brains),

Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications – but useful and practical implementations do not yet exist.

Independent from military-inspired research like DARPA’s, nanotechnology researchers in France have developed a hybrid nanoparticle-organic transistor that can mimic the main functionalities of a synapse. This organic transistor, based on pentacene and gold nanoparticles and termed NOMFET (Nanoparticle Organic Memory Field-Effect Transistor), has opened the way to new generations of neuro-inspired computers, capable of responding in a manner similar to the nervous system (read more: “Scientists use nanotechnology to try building computers modeled after the brain”).

One of the key components of any neuromorphic effort, and its starting point, is the design of artificial synapses. Synapses dominate the architecture of the brain and are responsible for massive parallelism, structural plasticity, and robustness of the brain. They are also crucial to biological computations that underlie perception and learning. Therefore, a compact nanoelectronic device emulating the functions and plasticity of biological synapses will be the most important building block of brain-inspired computational systems.

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In 2011, a team at Stanford University demonstrates a new single element nanoscale device, based on the successfully commercialized phase change material technology, emulating the functionality and the plasticity of biological synapses. In their work, the Stanford team demonstrated a single element electronic synapse with the capability of both the modulation of the time constant and the realization of the different synaptic plasticity forms while consuming picojoule level energy for its operation (read more: “Brain-inspired computing with nanoelectronic programmable synapses”).

Berger does mention memristors but not in any great detail in this article,

Researchers have also suggested that memristor devices are capable of emulating the biological synapses with properly designed CMOS neuron components. A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. It has the special property that its resistance can be programmed (resistor) and subsequently remains stored (memory).

One research project already demonstrated that a memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems (read more: “Nanotechnology’s road to artificial brains”).

Getting back to Berger’s April 4, 2014 article, he mentions one more approach and this one stands out,

A completely different – and revolutionary – human brain model has been designed by researchers in Japan who introduced the concept of a new class of computer which does not use any circuit or logic gate. This artificial brain-building project differs from all others in the world. It does not use logic-gate based computing within the framework of Turing. The decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.

In a previous Nanowerk Spotlight we reported on the concept of a full-fledged massively parallel organic computer at the nanoscale that uses extremely low power (“Will brain-like evolutionary circuit lead to intelligent computers?”). In this work, the researchers created a process of circuit evolution similar to the human brain in an organic molecular layer. This was the first time that such a brain-like ‘evolutionary’ circuit had been realized.

The research team, led by Dr. Anirban Bandyopadhyay, a senior researcher at the Advanced Nano Characterization Center at the National Institute of Materials Science (NIMS) in Tsukuba, Japan, has now finalized their human brain model and introduced the concept of a new class of computer which does not use any circuit or logic gate.

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In a new open-access paper published online on January 27, 2014, in Information (“Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System”), Bandyopadhyay and his team now describe the fundamental computing principle of a frequency fractal brain like computer.

“Our artificial brain-building project differs from all others in the world for several reasons,” Bandyopadhyay explains to Nanowerk. He lists the four major distinctions:
1) We do not use logic gate based computing within the framework of Turing, our decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.
2) We do not need to write any software, the argument and basic phase transition for decision-making, ‘if-then’ arguments and the transformation of one set of arguments into another self-assemble and expand spontaneously, the system holds an astronomically large number of ‘if’ arguments and its associative ‘then’ situations.
3) We use ‘spontaneous reply back’, via wireless communication using a unique resonance band coupling mode, not conventional antenna-receiver model, since fractal based non-radiative power management is used, the power expense is negligible.
4) We have carried out our own single DNA, single protein molecule and single brain microtubule neurophysiological study to develop our own Human brain model.

I encourage people to read Berger’s articles on this topic as they provide excellent information and links to much more. Curiously (mind you, it is easy to miss something), he does not mention James Gimzewski’s work at the University of California at Los Angeles (UCLA). Working with colleagues from the National Institute for Materials Science in Japan, Gimzewski published a paper about “two-, three-terminal WO3-x-based nanoionic devices capable of a broad range of neuromorphic and electrical functions”. You can find out more about the paper in my Dec. 24, 2012 posting titled: Synaptic electronics.

As for the ‘brain jelly’ paper, here’s a link to and a citation for it,

As for anyone who’s curious about why the US BRAIN initiative ((Brain Research through Advancing Innovative Neurotechnologies, also referred to as the Brain Activity Map Project) is not mentioned, I believe that’s because it’s focussed on biological brains exclusively at this point (you can check its Wikipedia entry to confirm).

Apparently, the prime motivation for listening to individual neurons or brain cells is to “better understand the computational complexity of the brain,” according to a June 20, 2013 news item on Azonano,

The new brain cell spear is a millimeter long, only a few nanometers wide and harnesses the superior electromechanical properties of carbon nanotubes to capture electrical signals from individual neurons.

“To our knowledge, this is the first time scientists have used carbon nanotubes to record signals from individual neurons, what we call intracellular recordings, in brain slices or intact brains of vertebrates,” said Bruce Donald, a professor of computer science and biochemistry at Duke University who helped developed the probe.

Currently, they use two main types of electrodes, metal and glass, to record signals from brain cells. Metal electrodes record spikes from a population of brain cells and work well in live animals. Glass electrodes also measure spikes, as well as the computations individual cells perform, but are delicate and break easily.”The new carbon nanotubes combine the best features of both metal and glass electrodes. They record well both inside and outside brain cells, and they are quite flexible. Because they won’t shatter, scientists could use them to record signals from individual brain cells of live animals,” said Duke neurobiologist Michael Platt, who was not involved in the study.

This is not the first time researchers have tried to use carbon nanotubes for this purpose, from the news release,

In the past, other scientists have experimented with carbon nanotube probes. But the electrodes were thick, causing tissue damage, or they were short, limiting how far they could penetrate into brain tissue. They could not probe inside individual neurons.

To change this, Donald began working on a harpoon-like carbon-nanotube probe with Duke neurobiologist Richard Mooney five years ago. The two met during their first year at Yale in the 1976, kept in touch throughout graduate school and began meeting to talk about their research after they both came to Duke.

Mooney told Donald about his work recording brain signals from live zebra finches and mice. The work was challenging, he said, because the probes and machinery to do the studies were large and bulky on the small head of a mouse or bird.

With Donald’s expertise in nanotechnology and robotics and Mooney’s in neurobiology, the two thought they could work together to shrink the machinery and improve the probes with nano-materials.

To make the probe, graduate student Inho Yoon and Duke physicist Gleb Finkelstein used the tip of an electrochemically sharpened tungsten wire as the base and extended it with self-entangled multi-wall carbon nanotubes to create a millimeter-long rod. The scientists then sharpened the nanotubes into a tiny harpoon using a focused ion beam at North Carolina State University.

Yoon then took the nano-harpoon to Mooney’s lab and jabbed it into slices of mouse brain tissue and then into the brains of anesthetized mice. The results show that the probe transmits brain signals as well as, and sometimes better than, conventional glass electrodes and is less likely to break off in the tissue. The new probe also penetrates individual neurons, recording the signals of a single cell rather than the nearest population of them.

Based on the results, the team has applied for a patent on the nano-harpoon. Platt said scientists might use the probes in a range of applications, from basic science to human brain-computer interfaces and brain prostheses.

Donald said the new probe makes advances in those directions, but the insulation layers, electrical recording abilities and geometry of the device still need improvement.

As for calling this a ‘harpoon’, these carbon nanotube probes really do resemble harpoons,

This image, taken with a scanning electron microscope, shows a new brain electrode that tapers to a point as thick as a single carbon nanotube. Credit: Inho Yoon and Bruce Donald, Duke. [downloaded from http://today.duke.edu/2013/06/brainharpoon]

They’ve managed to recreate Pavlov’s classic experiment with dogs and feeding bells using an electronic circuit and teaching it to respond to a stimulus just as the dogs learned to respond. From the May 8, 2012 news item on Science Daily,

The bell rings and the dog starts drooling. Such a reaction was part of studies performed by Ivan Pavlov, a famous Russian psychologist and physiologist and winner of the Nobel Prize for Physiology and Medicine in 1904. His experiment, nowadays known as “Pavlov’s Dog,” is ever since considered as a milestone for implicit learning processes. By using specific electronic components scientists form the Technical Faculty and the Memory Research at the Kiel University together with the Forschungszentrum Jülich were now able to mimic the behavior of Pavlov`s dog.

Also from the May 8, 2012 news release on the University of Kiel website,

“We used memristive devices in order to mimic the associative behaviour of Pavlov’s dog in form of an electronic circuit”, explains Professor Hermann Kohlstedt, head of the working group Nanoelectronics at the University of Kiel.

Memristors are a class of electronic circuit elements which have only been available to scientists in an adequate quality for a few years. They exhibit a memory characteristic in form of hysteretic current-voltage curves consisting of high and low resistance branches. In dependence on the prior charge flow through the device these resistances can vary. Scientists try to use this memory effect in order to create networks that are similar to neuronal connections between synapses. “In the long term, our goal is to copy the synaptic plasticity onto electronic circuits. We might even be able to recreate cognitive skills electronically”, says Kohlstedt. The collaborating scientific working groups in Kiel and Jülich have taken a small step toward this goal.

The project set-up consisted of the following: two electrical impulses were linked via a memristive device to a comparator. The two pulses represent the food and the bell in Pavlov’s experiment. A comparator is a device that compares two voltages or currents and generates an output when a given level has been reached. In this case, it produces the output signal (representing saliva) when the threshold value is reached. In addition, the memristive element also has a threshold voltage that is defined by physical and chemical mechanisms in the nano-electronic device. Below this threshold value the memristive device behaves like any ordinary linear resistor. However, when the threshold value is exceeded, a hysteretic (changed) current-voltage characteristic will appear.

“During the experimental investigation, the food for the dog (electrical impulse 1) resulted in an output signal of the comparator, which could be defined as salivation. Unlike to impulse 1, the ring of the bell (electrical impulse 2) was set in such a way that the compartor’s output stayed unaffected – meaning no salivation”, describes Dr. Martin Ziegler, scientist at the Kiel University and the first-author of the publication. After applying both impulses simultaneously to the memristive device, the threshold value was exceeded. The working group had activated the memristive memory function. Multiple repetitions led to an associative learning process within the circuit – similar to Pavlov’s dogs. “From this moment on, we had only to apply electrical impulse 2 (bell) and the comparator generated an output signal, equivalent to salivation”, says Ziegler and is very pleased with these results. Electrical impulse 1 (feed) triggers the same reaction as it did before the learning. Hence, the electric circuit shows a behaviour that is termed classical conditioning in the field of psychology. Beyond that, the scientists were able to prove that the electrical circuit is able to unlearn a particular behaviour if both impulses were not longer applied simultaneously.

My most recent posting (and I have many) on memristors is from April 19, 2012 where I mentioned an artificial synapse developed with them at the University of Michigan and also noted that HP Labs has claimed it will be releasing ‘memristor-based’ products in2013.

The May 8, 2012 news item on Science Daily includes the full citation for the team’s paper and a link to it (the paper is behind a paywall).

Swedish scientists have announced success with growing nerve cells using nanocellulose as the scaffolding. From the March 19, 2012 news item on Naowerk,

Researchers from Chalmers and the University of Gothenburg have shown that nanocellulose stimulates the formation of neural networks. This is the first step toward creating a three-dimensional model of the brain. Such a model could elevate brain research to totally new levels, with regard to Alzheimer’s disease and Parkinson’s disease, for example.

…

“This has been a great challenge,” says Paul Gatenholm, Professor of Biopolymer Technology at Chalmers.?Until recently the cells were dying after a while, since we weren’t able to get them to adhere to the scaffold. But after many experiments we discovered a method to get them to attach to the scaffold by making it more positively charged. Now we have a stable method for cultivating nerve cells on nanocellulose.”

When the nerve cells finally attached to the scaffold they began to develop and generate contacts with one another, so-called synapses. A neural network of hundreds of cells was produced. The researchers can now use electrical impulses and chemical signal substances to generate nerve impulses, that spread through the network in much the same way as they do in the brain. They can also study how nerve cells react with other molecules, such as pharmaceuticals.

Nerve cells growing on a three-dimensional nanocellulose scaffold. One of the applications the research group would like to study is destruction of synapses between nerve cells, which is one of the earliest signs of Alzheimer’s disease. Synapses are the connections between nerve cells. In the image, the functioning synapses are yellow and the red spots show where synapses have been destroyed. Illustration: Philip Krantz, Chalmers

This latest research from Gatenholm and his team will be presented at the American Chemical Society annual meeting in San Diego, March 25, 2012.

The research team from Chalmers University and its partners are working on other applications for nanocellulose including one for artificial ears. From the Chalmers University Jan. 22, 2012 press release,

As the first group in the world, researchers from Chalmers will build up body parts using nanocellulose and the body’s own cells. Funding will be from the European network for nanomedicine, EuroNanoMed.

Professor Paul Gatenholm at Chalmers is leading and co-ordinating this European research programme, which will construct an outer ear using nanocellulose and a mixture of the patient’s own cartilage cells and stem cells.

…

Previously, Paul Gatenholm and his colleagues succeeded, in close co-operation with Sahlgrenska University Hospital, in developing artificial blood vessels using nanocellulose, where small bacteria “spin” the cellulose.

In the new programme , the researchers will build up a three-dimensional nanocellulose network that is an exact copy of the patient’s healthy outer ear and construct an exact mirror image of the ear. It will have sufficient mechanical stability for it to be used as a bioreactor, which means that the patient’s own cartilage and stem cells can be cultivated directly inside the body or on the patient, in this case on the head. [Presumably the patient has one ear that is healthy and the researchers are attempting to repair or replace an unhealthy ear on the other side of the head.]

As for the Swedish perspective on nanocellulose (from the 2010 press release),

Cellulose-based material is of strategic significance to Sweden and materials science is one of Chalmers eight areas of advance. Biopolymers are highly interesting as they are renewable and could be of major significance in the development of future materials.

Further research into using the forest as a resource for new materials is continuing at Chalmers within the new research programme that is being built up with different research groups at Chalmers and Swerea – IVF. The programme is part of the Wallenberg Wood Science Center, which is being run jointly by the Royal Institute of Technology in Stockholm and Chalmers under the leadership of Professor Lars Berglund at the Royal Institute of Technology.

The 2012 press release announcing the work on nerve cells had this about nanocellulose,

Nanocellulose is a material that consists of nanosized cellulose fibers. Typical dimensions are widths of 5 to 20 nanometers and lengths of up to 2,000 nanometers. Nanocellulose can be produced by bacteria that spin a close-meshed structure of cellulose fibers. It can also be isolated from wood pulp through processing in a high-pressure homogenizer.

I last wrote about the Swedes and nanocellulose in a Feb. 15, 2012 posting about recovering it (nanocellulose) from wood-based sludge.

As for anyone interested in the Canadian scene, there is an article by David Manly in the Jan.-Feb. 2012 issue of Canadian Biomass Magazine that focuses largely on economic impacts and value-added products as they pertain to nanocellulose manufacturing production in Canada. You can also search this blog as I have covered the nanocellulose story in Canada and elsewhere as extensively as I can.

Carbon nanotubes, like the nervous cells of our brain, are excellent electrical signal conductors and can form intimate mechanical contacts with cellular membranes, thereby establishing a functional link to neuronal structures. …

Now, researchers have, for the first time, explored the impact of carbon nanotube scaffolds on multilayered neuronal networks. Up to now, all known effects of carbon nanotubes on neurons – namely their reported ability to potentiate neuronal signaling and synapses – have been described in bi-dimensional cultured networks where nanotube/neuron hybrids were developed on a monolayer of dissociated brain cells.

In their work, a team of scientists in Italy, led by professors Maurizio Prato and Laura Ballerini, used slices from the spinal cords of mice to model multilayer-tissue complexity. They interfaced these spinal segments to multi-walled carbon nanotube (MWCNT) scaffolds for weeks at a time to see whether and how the interactions at the monolayer level are translated to multilayered nerve tissues.

I found this part of the explanation a little easier to understand,

According to the team, interfacing spinal cord explants [cells removed from living tissue and cultivated in artificial media] to purified carbon nanotubes over a longer period (weeks) induces two major effects: First, the number and length of neuronal fibers outgrowing the spinal segment increases, associated with changes in growth cone activity and in fiber elastomechanical properties. And, secondly, the researchers point out that after weeks of MWCNT interfacing, neurons located at as far as five cell layers from the substrate display an increased efficacy in synaptic responses – which could represent either an improvement or a pathological behavior – presumably mediated by ongoing plasticity driven by the neuron/MWCNT hybrids.

If this increased efficacy in synaptic responses should represent an improvement, it suggests to me that it could be helpful with spinal cord injuries at some point. The researchers themselves are not speculating that far into the future (from the Berger essay),

They [Prato and Ballerini] note that this is important because it exploits the design of artificial micro- and nanoscale devices that cooperate with neuronal network activity, thereby creating hybrid structures able to cross the barriers between artificial devices and neurons.

Taken in conjunction with today’s (March 5, 2012) earlier posting (Carbon and neural implants), it seems that there is a great deal of work being done to integrate ‘machine’ and flesh so we achieve machine/flesh. While I don’t believe that philosopher and chemist Isabelle Stengers will be addressing those specific issues in her talk, Cosmopolitics, being livestreamed here later today (3:30 pm PST) from Halifax (Nova Scotia), she does touch on this,

Professor Stengers’ keynote address will examine sciences and the consequences of what has been called progress. Is it possible to reclaim modern practices, to have them actively taking into account what they felt entitled to ignore in the name of progress? Or else, can they learn to “think with” instead of define and judge? [emphasis mine]

I don’t know what she means by ‘think with’ but it strikes me that it represents a significant shift of thought as it implies a relationship that is not separated (or bounded) in the ways we have traditionally observed. Defining and judging are made possible by the notion of separation (boundaries); machine and flesh have been viewed from the perspective of boundaries and separation; machine/flesh seems more like ‘thinking with’.